Tag: Marcia Villalba

AWS Machine Learning University is now providing a free educator enablement program. This program provides faculty at community colleges, minority-serving institutions (MSIs), and historically Black colleges and universities (HBCUs) with the skills and resources to teach data analytics, artificial intelligence (AI), and machine learning (ML) concepts to build a diverse pipeline for in-demand jobs of today and tomorrow. According to the National Science Foundation, Black and Hispanic or Latino students earn bachelor’s degrees in Computer Science—the dominant pathway to AI/ML—at a much lower rate than their white peers, earning less than 11 percent of computer science degrees awarded. However, research shows that having diverse perspectives among skilled practitioners and across the AI/ML lifecycle contributes to the development of AI/ML systems…

Today, we’re announcing AWS SimSpace Weaver, a new compute service to run real-time spatial simulations in the cloud and at scale. With SimSpace Weaver, simulation developers are no longer limited by the compute and memory of their hardware. Organizations run simulations on situations that are rare, dangerous, or very expensive to test in the real world. For example, city managers can’t wait for a natural disaster to hit a city to test the response systems. Event planners don’t want to wait until a large sporting event to start to understand the impact the games will have on traffic. Scenarios like these need to be simulated in a safe environment in which planners can test different situations and tune each system.…

Amazon Inspector is a vulnerability management service that continually scans workloads across Amazon Elastic Compute Cloud (Amazon EC2) instances, container images living in Amazon Elastic Container Registry (Amazon ECR), and, starting today, AWS Lambda functions and Lambda layers. Until today, customers that wanted to analyze their mixed workloads (including EC2 instances, container images, and Lambda functions) against common vulnerabilities needed to use AWS and third-party tools. This increased the complexity of keeping all their workloads secure. In addition, the log4j vulnerability a few months ago was a great example that scanning your functions for vulnerabilities only before deployment is not enough. Because new vulnerabilities can appear at any time, it is very important for the security of your applications that…

Today we are announcing Amazon CloudWatch Logs data protection, a new set of capabilities for Amazon CloudWatch Logs that leverage pattern matching and machine learning (ML) to detect and protect sensitive log data in transit. While developers try to prevent logging sensitive information such as Social Security numbers, credit card details, email addresses, and passwords, sometimes it gets logged. Until today, customers relied on manual investigation or third-party solutions to detect and mitigate sensitive information from being logged. If sensitive data is not redacted during ingestion, it will be visible in plain text in the logs and in any downstream system that consumed those logs. Enforcing prevention across the organization is challenging, which is why quick detection and prevention of…